Artificial intelligence in surgical care within low-income and middle-income countries: a scoping review of development, validation, and deployment. [Review]

Valli, AD.; Tingle, SJ.; Kazerouni, S.; Raj Kalpana, TS.; Karki, B.; Knight, SR.; Wilson, C.; Kourounis, G.

Artificial intelligence in surgical care within low-income and middle-income countries: a scoping review of development, validation, and deployment. [Review]

All Authors

Valli, AD.

Tingle, SJ.

Kazerouni, S.

Raj Kalpana, TS.

Karki, B.

Knight, SR.

Wilson, C.

Kourounis, G.

LTHT Author

Karki, Bishow

LTHT Department

Drs Rotation
General Surgery

Publication Date

2026

Item Type

Journal Article Review

Subject

ARTIFICIAL INTELLIGENCE , DEVELOPING COUNTRIES , GLOBAL HEALTH , HEALTH INFORMATION SYSTEMS , MEDICAL INFORMATICS , SURGICAL PROCEDURES, OPERATIVE

Abstract

Artificial intelligence (AI) has the potential to expand access to high-quality surgical care in low-income and middle-income countries (LMICs), yet the extent and maturity of AI research in these settings remain unclear. We conducted a prospectively registered scoping review (osf.io/9PV6A) to synthesize primary evidence on the use of AI in LMIC surgical care. PubMed, Scopus, and Web of Science were searched for studies evaluating AI in surgical contexts within LMICs up to July 14, 2025. From 2602 records, 475 studies met inclusion criteria. Most were conducted in upper-middle-income countries (n = 376, 79.1%), with the overwhelming majority from China (n = 305, 64.2%). Only 46 studies (9.7%) were conducted in lower-middle-income countries and 5 (1.1%) in low-income countries. Research was predominantly retrospective (68%), and only nine randomised controlled trials were identified (2%). Most studies focused on model development (67%), with few reporting external validation (30%) or clinical deployment (3%), mostly as pilot trial-based integrations. Barriers to AI implementation included fragmented data systems, limited infrastructure, and workforce constraints. Facilitators included widespread smartphone access and growing international collaborations. Despite rapid growth, AI research remains in the early stages of development. Focus on model accuracy alone is insufficient if health systems lack the capacity for adoption and integration.